The Role of Artificial Intelligence in Precision Medicine: Bridging Data Science and Patient Care

Abstract

Precision medicine has revolutionized healthcare by moving beyond the one-size-fits-all approach to embrace individualized treatment strategies based on genetic, environmental, and lifestyle factors. The integration of Artificial Intelligence (AI) has significantly accelerated this transformation by enabling the analysis of vast, complex datasets with unprecedented speed and accuracy. AI-driven techniques, including deep learning, machine learning, and natural language processing, play a key role in data integration, diagnostics, drug discovery, and treatment optimization. For instance, AI enhances cancer diagnostics by detecting patterns in imaging data and predicting patient responses to immunotherapy. In addition, wearable devices and electronic health records (EHRs) provide real-time, patient-specific insights that AI systems use to create personalized care pathways. Despite its transformative potential, AI in precision medicine faces challenges such as data privacy concerns, algorithmic bias, and regulatory hurdles, which require careful navigation to ensure equitable and ethical implementation. Emerging trends, including explainable AI and collaborative interdisciplinary research, promise to address these challenges and foster trust among clinicians and patients. This narrative review explores the role of AI in bridging data science and patient care within the framework of precision medicine, emphasizing its potential to deliver tailored, effective, and equitable healthcare solutions.